Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James
McKelvey School of Engineering Theses & Dissertations
Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …
Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu
Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu
McKelvey School of Engineering Theses & Dissertations
Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …
Application Of Crowdsourcing And Machine Learning To Predict Sentiments In Textual Student Feedback In Large Computer Science Classes, Robert Kasumba
Application Of Crowdsourcing And Machine Learning To Predict Sentiments In Textual Student Feedback In Large Computer Science Classes, Robert Kasumba
McKelvey School of Engineering Theses & Dissertations
With the increasing enrollment numbers into popular computer science courses, there is a need to bridge the similarly increasing feedback gap between individual students and course instructors. One way to address this challenge is for instructors to collect feedback from students in form of textual reviews or unit-of-study reflections – however, manually reading these reviews is time-consuming, and self-reported Likert scale responses are noisy. Rule-based approaches to sentiment analysis such as VADER (Valence Aware Dictionary and sEntiment Reasoner) have been used to capture the sentiments conveyed in textual feedback, they however fail to capture contextual differences as many words have …